Economic disparity among generations under the Paris Agreement

The study investigates how the lifetime economic costs and benefits of climate change mitigation under the Paris Agreement are distributed across generations and nations. Motivated by the prominence of younger generations in climate activism and narratives that they bear disproportionate future climate damages, the paper addresses a gap in quantifying intergenerational cost-benefit outcomes at the country level. The authors define mitigation costs as GDP losses relative to a no-mitigation baseline and benefits as avoided economic damages from limiting warming. They aim to estimate, by age cohort and country, the net lifetime change in GDP per capita from climate mitigation, identify the breakeven generation where benefits exceed costs, and project how intergenerational disparities evolve over time. The work is positioned as critical for understanding equity and consensus-building across generations and nations in climate policy.

Prior literature highlights youth-led climate activism and perceived intergenerational injustice, with studies noting generational gaps in climate policy attitudes. Economic analyses have explored designing policies to reduce intergenerational disparities under climate mitigation. Integrated Assessment Models (IAMs) are widely used to estimate mitigation costs as GDP losses relative to baselines, with IPCC AR5 reporting global abatement costs of roughly 2–6% of global GDP by 2100 under stringent targets. For benefits, the Burke, Hsiang, and Miguel (BHM) damage function quantifies a nonlinear temperature–economic growth relationship and has been applied to estimate large avoided damages under mitigation, though its long-run specification remains under debate. The study builds on these strands, combining IAM-derived mitigation costs with BHM-based benefit estimates to quantify net lifetime effects by cohort across countries.

Scope and scenarios: The analysis covers 169 countries and age cohorts born between 1920 and 2020, evaluating outcomes over 2020–2100 under a mitigation scenario consistent with the Paris Agreement (RCP2.6) versus a pre-Paris baseline (SSP4 with RCP6.0 in the main analysis; other SSP/RCP combinations explored in sensitivity). Costs: Mitigation costs are defined as GDP losses relative to the baseline, drawing on IAM results compiled in IPCC AR5. Annual global GDP loss trajectories L_mitigation are constructed via linear interpolation between reported years (2020, 2030, 2050, 2100) and allocated to regions using regional cost ratios (five macro-regions: OECD 1990, Asia, Middle East & Africa, Latin America, Economies in Transition). Country-level costs are obtained by distributing regional totals and discounting over each cohort’s lifetime. A triangular distribution is assumed for uncertain cost parameters, and uncertainty is explored via Monte Carlo simulations. Benefits: Benefits are the avoided economic damages from reduced warming, estimated using the BHM damage function linking temperature to GDP growth. CMIP5 model outputs provide temperature changes under RCPs, corrected to country level using baseline observations and linear interpolation of warming between 2010 and 2100. Two benefit specifications are implemented: short-term (contemporary) and long-term BHM functions, reflecting differing assumptions about persistence and magnitude of temperature effects on growth. GDP growth with temperature effects is simulated annually to 2100 for baseline and mitigation to derive cumulative GDP per capita differences. Cohorts and lifetime metrics: For each cohort, lifetime is computed using age-specific life expectancy from the UN and World Bank, truncated within 2020–2100. Lifetime cumulative GDP per capita is calculated by summing discounted annual GDP per capita over the cohort’s remaining lifetime at a 3% discount rate (with sensitivity to 5% and growth-adjusted rates). Income distribution across age groups is incorporated using OECD age–income profiles; for countries lacking data, the median profile from developing countries in the OECD database is used, and the profile is assumed time-invariant. Key derived indicators: - Net lifetime percentage change in GDP per capita (RGDP) for each cohort and country under mitigation relative to baseline. - Breakeven generation (BY): the cohort whose net lifetime benefits first become positive; if none, marked nonexistence. - Intergenerational disparity index (IDI): percentage change for the 25-year-old cohort minus that for the 75-year-old cohort in a given year; projected from 2020 to 2100. Uncertainty and sensitivity: The study assesses sensitivity to SSP/RCP choices, discount rates (fixed 3% and 5%, growth-adjusted), BHM parameter uncertainty via bootstrapping, cost parameter distributions, and model specification of the temperature–growth function. Results and data are provided via a public repository and an interactive app.

• Older cohorts generally lose, younger cohorts often gain: Across nearly all nations, cohorts born before 1960 experience net reductions in lifetime GDP per capita from mitigation, while in most lower-income countries, cohorts born after 1990 see net gains. - Magnitudes by income group (short-term BHM benefits): • Low-income countries: cohorts born 1920–1960 incur on average ~2.5% net reduction in lifetime GDP per capita. Cohort born in 1950: ~−3%; cohort born in 2020: ~+6% on average. • Lower-middle-income countries: younger cohorts’ largest net gains are 5–8 times larger (in absolute value) than older cohorts’ net reductions. • High-income countries: all cohorts, on average, lose 0–2% of lifetime GDP per capita. • Upper-middle-income countries: all cohorts, on average, lose 0–3%. - Sensitivity to benefit specification: Using the long-term BHM function increases estimated benefits in richer countries. • High-income: cohorts born in 1960 and later show net gains on average. • Upper-middle-income: cohorts born in 1980 and later show net gains on average. However, the long-term function has wider uncertainty than the short-term. - Breakeven generations (short-term benefits): • Latin America and South Asia: breakeven cohorts born before 1970; >75% of current population born after breakeven. • Eastern Europe: only cohorts born after 1980 enjoy net benefits; >50% of current population born before breakeven. • High-income: Spain, Australia, Saudi Arabia: breakeven before 1980. Canada, most of Western Europe: no breakeven among 1920–2020 cohorts; United States: breakeven at 1994 (short-term). Under long-term benefits, breakeven shifts earlier (before 1970) in Canada, the US, and Western Europe. • Upper-middle-income: Russia and South Africa have youngest or nonexistent breakeven (after 1990 or none), with only 0–31% of population after breakeven in Russia; Brazil and Mexico have breakeven before 1970 with >75% of population after breakeven. China: no breakeven under short-term; two-thirds of population after breakeven under long-term. • Lower-middle and low-income: breakeven generally before 1990; as these populations are younger, more than half are born after breakeven in most countries. South Asia and Latin America have oldest breakeven (1950–1970 in India, Pakistan, Bolivia); Southeast Asia before 1980; Africa 1980–1990 (10–30 years younger than Latin America/South Asia). - Intergenerational disparity index (IDI), short-term benefits: • Disparity widens over time and is larger in lower-income countries. Median IDI: lower-middle-income rises from 0.05 (2020) to 0.42 (2100); low-income from 0.06 to 0.52; upper-middle-income from 0.05 to 0.27. • High-income countries show the smallest disparity: IDI mostly within −0.25 to 0.25; median IDI in 2100 is −0.04 (indicating relative favoring of older cohorts). • Regionally, Latin America, Africa, and Western/Southern Asia see the largest increases (from <0.1 to >0.25 by 2100). Some countries (Saudi Arabia, Sudan, Niger, Mauritius) exceed IDI of 1; Eastern Asia, Europe, North America remain <0.25. - Intergenerational disparity under long-term benefits: Similar pattern of widening disparities. For example, in low-income countries, median IDI increases from 0.04 (2020) to 0.34 (2100); across other income groups, medians rise from around 0.05 to approximately 0.31, 0.28, and 0.23.

The findings reveal pronounced intergenerational asymmetries in the economic impacts of climate mitigation. Globally, cohorts born before 1960 see little or negative net lifetime gains, while those born after 1990—especially in lower-income countries—benefit substantially, aligning with narratives that younger generations have stronger incentives to support mitigation. However, national patterns complicate this narrative: under short-term damage specifications, many Western European countries show no net-benefit cohorts among those born 1920–2020, suggesting that European youth activism cannot be fully explained by short-run economic self-interest. Under long-term damage assumptions, younger cohorts in these countries do benefit, highlighting the importance of damage specification. The projected widening of the intergenerational disparity index implies growing economic divisions between young and old under mitigation, particularly in lower-income countries. This may shape political economy dynamics: where most of the population is born after breakeven (many lower-income countries), older generations may be more supportive of mitigation due to expected net benefits, whereas regions like Eastern Europe may face political challenges because less than half of the population stands to gain. Overall, the results underscore the difficulty of forging intergenerational and international consensus on climate policy and suggest that policy design should account for heterogeneous impacts across cohorts and countries.

This study quantifies, for 169 countries, the lifetime net economic impacts of Paris-consistent climate mitigation by birth cohort, introducing cohort-specific net gains, breakeven generations, and an intergenerational disparity index. It finds that older cohorts (pre-1960) typically bear net costs, younger cohorts (post-1990) in lower-income countries gain, many higher-income countries have no net-benefit cohorts under short-term damages, and intergenerational disparities widen markedly through 2100, especially in lower-income countries. These insights highlight the need to incorporate intergenerational equity into climate policy design, potentially via differentiated tax and fiscal policies, and to consider how asset ownership (e.g., renewable assets) mediates cohort impacts. Future research could refine long-term damage estimates, integrate policies targeted at mitigating intergenerational disparities, and extend analyses to alternative socio-economic pathways, adaptive capacity, and distributional effects within cohorts.

• Benefit specification uncertainty: Long-term BHM damage function yields wider uncertainty and remains debated; results for richer countries depend strongly on whether short-term or long-term effects are assumed. - Scenario dependence: Baseline (SSP4/RCP6.0) and mitigation (RCP2.6) choices influence outcomes; alternative SSP/RCPs are explored, but uncertainty remains. - Discounting: Results depend on discount rate assumptions (3% baseline, 5% and growth-adjusted in sensitivity). - Data and model simplifications: Income-by-age profiles derived from OECD; for many developing countries, median profiles are imputed and assumed time-invariant. Life expectancy and cohort lifetimes are truncated to 2100. - Cost allocation and distributions: Global IAM-derived costs are regionally allocated with assumed ratios; triangular distributions for uncertain cost parameters may not capture full variability. - Policy exclusion: Scenarios do not include targeted intergenerational redistribution or compensatory policies that could materially affect net outcomes. - Temperature–growth model specification: Alternative functional forms and differentiation between rich and poor countries introduce additional uncertainty not fully resolved.